Instructions to use ProbeX/Model-J__ResNet__model_idx_0140 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0140 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0140") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0140") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0140", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d83521274b4c4966ac9fad4421cda5c86ca48ba6e11f9dc174affbd102c2cf13
- Size of remote file:
- 171 MB
- SHA256:
- bb4092e18436b59089830eefde12e3866caae58ce0be621a420cbe0dca1ca57e
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